R: A Fast and Flexible Pipeline for Text Classification

# A Fast and Flexible Pipeline for Text Classification

## Documentation for package ‘quickSentiment’ version 0.3.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

BOW_test | Transform New Text into a Document-Feature Matrix
BOW_train | Train a Bag-of-Words Model
evaluate_performance | Evaluate Model Performance (ROC and Precision-Recall)
logit_model | Train a Regularized Logistic Regression Model using glmnet
nb_model | Multinomial Naive Bayes for Text Classification
pipeline | Run a Full Text Classification Pipeline on Preprocessed Text
plot.quickSentiment_prc | Plot Precision-Recall Curve
plot.quickSentiment_roc | Plot ROC Curve
predict_sentiment | Predict Sentiment on New Data Using a Saved Pipeline Artifact
pre_process | Preprocess a Vector of Text Documents
print.quickSentiment_eval | Print quickSentiment Evaluation Results
qs_negations | Standard Negation Words for Sentiment Analysis
rf_model | functions/random_forest_fast.R Train a Random Forest Model using Ranger
xgb_model | Train a Gradient Boosting Model using XGBoost